Campaign 2017 (PhD Chapter 1)
Analyses and regressions
This series of files compile all analyses done during Chapter 1 for the regional campaign of 2017:
All analyses have been done with PRIMER-e 6 and R 3.6.0.
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Caracteristics of each campaign
| 2014 | 2017 | 2017 | ||
|---|---|---|---|---|
| Sampling date | August-September | June to August | July | |
| Criteria for perturbation | Potentially impacted if close to the city or industries, References outside the bay | Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria | Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria | |
| Regions considered | BSI | BSI, CPC, BDA, MR | BSI, MR | |
| Number of sampled stations | 40 (20 HI, 20 R) | 78 (26 BSI, 19 CPC, 18 BDA, 15 MR) | 126 (111 BSI, 15 MR) | |
| Parameters sampled | Organic matter | yes | yes | yes |
| Photosynthetic pigments | no | yes | yes | |
| Sediment grain-size | yes | yes | yes | |
| Heavy-metals | yes | yes (for a limited number of stations) | no (interpolated based on 2014 and 2017 values) | |
| Benthic communities | Compartment targeted | Macro-infauna | Macro-infauna | Macro-infauna |
| Sieved used | 500 µm | 1 mm | 500 µm and 1 mm | |
| Conservation technique | Formaldehyle | Formaldehyle | Formaldehyle | |
| Others | N.A. | N.A. | N.A. |
We used data from subtidal ecosystems (see metadata files for more information). Only stations that have been sampled both for abiotic parameters and benthic species were included.
Selected variables for the analyses:
- Depth of the station: depth (only for ANCOVAs)
- Percentage of organic matter: om
- Percentage of gravel: gravel
- Percentage of sand: sand
- Percentage of silt: silt
- Percentage of clay: clay
- Concentration of arsenic: arsenic
- Concentration of cadmium: cadmium
- Concentration of chromium: chromium
- Concentration of copper: copper
- Concentration of iron: iron
- Concentration of manganese: manganese
- Concentration of mercury: mercury
- Concentration of lead: lead
- Concentration of zinc: zinc
- Species richness: S (only for 500 µm communities)
- Abundance of total individuals: N (only for 500 µm communities)
- Shannon index: H (only for 500 µm communities)
- Piélou evenness: J (only for 500 µm communities)
We only considered 500 µm communities for these analyses. Abundances of Bipalponephtys neotena (Bneo) and Nematoda (Nema) were also considered (see IndVal and SIMPER results).
Heavy metal concentrations for campaign 2017 have been kriged from the values collected at campaigns 2014 and 2016. As data is missing for metal concentrations outside BSI, two Designs have been used:
- Design 1: stations at BSI, MR with habitat parameters
- Design 2: stations at BSI with heavy metal concentrations.
Statistics for each variable considered:
| Mean | SD | SE | Median | Min | Max | 95% CI | |
|---|---|---|---|---|---|---|---|
| depth | 22.364 | 18.168 | 2.909 | 20.900 | 1.600 | 66.900 | 5.702 |
| om | 0.885 | 0.726 | 0.116 | 0.620 | 0.186 | 3.872 | 0.228 |
| gravel | 0.028 | 0.115 | 0.018 | 0.000 | 0.000 | 0.701 | 0.036 |
| sand | 0.649 | 0.271 | 0.043 | 0.675 | 0.005 | 0.992 | 0.085 |
| silt | 0.304 | 0.239 | 0.038 | 0.322 | 0.007 | 0.869 | 0.075 |
| clay | 0.020 | 0.084 | 0.014 | 0.000 | 0.000 | 0.407 | 0.026 |
| S_500 | 10.436 | 4.994 | 0.800 | 11.000 | 1.000 | 23.000 | 1.567 |
| N_500 | 79.795 | 80.331 | 12.863 | 58.000 | 1.000 | 382.000 | 25.212 |
| H_500 | 1.588 | 0.620 | 0.099 | 1.643 | 0.000 | 2.568 | 0.194 |
| J_500 | 0.700 | 0.210 | 0.034 | 0.738 | 0.000 | 1.000 | 0.066 |
| Mean | SD | SE | Median | Min | Max | 95% CI | |
|---|---|---|---|---|---|---|---|
| arsenic | 3.908 | 3.085 | 0.630 | 2.750 | 1.500 | 16.000 | 1.234 |
| cadmium | 0.150 | 0.041 | 0.008 | 0.145 | 0.090 | 0.230 | 0.017 |
| chromium | 55.163 | 20.949 | 4.276 | 54.750 | 28.000 | 110.700 | 8.381 |
| copper | 11.121 | 6.686 | 1.365 | 8.950 | 2.900 | 28.700 | 2.675 |
| iron | 49908.033 | 13633.036 | 2782.832 | 48883.150 | 28355.900 | 78471.300 | 5454.250 |
| manganese | 952.654 | 455.270 | 92.932 | 847.700 | 423.100 | 2098.000 | 182.142 |
| mercury | 0.024 | 0.016 | 0.003 | 0.021 | 0.008 | 0.087 | 0.006 |
| lead | 5.533 | 2.520 | 0.514 | 5.050 | 2.400 | 12.100 | 1.008 |
| zinc | 61.654 | 25.066 | 5.117 | 55.750 | 33.500 | 141.000 | 10.028 |
1. Data manipulation
For the following analyses, independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices.
1.1. Identification of outliers
To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.
Design 1
Based on Cook’s Distance, we identified stations 188, 194 and 228 as general outliers. They have been deleted for the following analyses of Design 1.
Design 2
Based on Cook’s Distance, we identified stations 132 and 154 as general outliers. They have been deleted for the following analyses of Design 2.
1.2. Correlations between parameters
Correlations have been calculated with Spearman’s rank coefficient.
Design 1
| om | gravel | sand | silt | clay | |
|---|---|---|---|---|---|
| om | 1 | -0.365 | -0.791 | 0.877 | 0.119 |
| gravel | -0.365 | 1 | 0.015 | -0.325 | 0.183 |
| sand | -0.791 | 0.015 | 1 | -0.896 | -0.47 |
| silt | 0.877 | -0.325 | -0.896 | 1 | 0.212 |
| clay | 0.119 | 0.183 | -0.47 | 0.212 | 1 |
According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions of Design 1:
- sand and silt (silt deleted)
Design 2
| arsenic | cadmium | chromium | copper | iron | manganese | mercury | lead | zinc | |
|---|---|---|---|---|---|---|---|---|---|
| arsenic | 1 | 0.658 | 0.838 | 0.801 | 0.701 | 0.598 | 0.438 | 0.8 | 0.874 |
| cadmium | 0.658 | 1 | 0.809 | 0.467 | 0.719 | 0.794 | 0.288 | 0.806 | 0.777 |
| chromium | 0.838 | 0.809 | 1 | 0.811 | 0.871 | 0.84 | 0.406 | 0.889 | 0.945 |
| copper | 0.801 | 0.467 | 0.811 | 1 | 0.688 | 0.573 | 0.308 | 0.805 | 0.896 |
| iron | 0.701 | 0.719 | 0.871 | 0.688 | 1 | 0.834 | 0.182 | 0.71 | 0.848 |
| manganese | 0.598 | 0.794 | 0.84 | 0.573 | 0.834 | 1 | 0.208 | 0.679 | 0.77 |
| mercury | 0.438 | 0.288 | 0.406 | 0.308 | 0.182 | 0.208 | 1 | 0.488 | 0.37 |
| lead | 0.8 | 0.806 | 0.889 | 0.805 | 0.71 | 0.679 | 0.488 | 1 | 0.924 |
| zinc | 0.874 | 0.777 | 0.945 | 0.896 | 0.848 | 0.77 | 0.37 | 0.924 | 1 |
Many variables are highly correlated (\(|\rho|\) > 0.80), but we have considered the following together in the regressions of Design 2:
- chromium, iron and manganese (iron and manganese deleted)
- copper, lead and zinc (copper and zinc deleted)
We decided to keep arsenic, even though it is correlated with the copper/lead/zinc group, to stay consistant with the 2014 and 2016 campaigns.
2. Permutational Analyses of Covariance
Results of univariate PermANCOVAs on parameters and multivariate PermANCOVA on the whole benthic community with depth as covariate are presented in the table below. Variables were normalized and abundances were (log+1) transformed.
| Variable | Condition | Depth |
|---|---|---|
| om | S | |
| gravel | ||
| sand | ||
| silt | S | |
| clay | ||
| S (500 µm) | S | |
| N (500 µm) | ||
| H (500 µm) | S | |
| J (500 µm) | S | |
| ALL SPECIES (500 µm) | S | S |
3. Similarity and characteristic species
Let’s have a look at the \(\beta\) diversity within our conditions and sites.
Results of the PERMDISP routine are shown below (mean and SE of the deviation from centroid for each group, i.e. multivariate dispersion), along with the mean Bray-Curtis dissimilarity for each group. Abundances were (log+1) transformed and PRIMER was used to do the PERMDISP.
| Mean deviation | SE of deviation | Mean BC dissimilarity | |
|---|---|---|---|
| HI | 57.5 | 1.64 | 0.829 |
| R | 48.8 | 2.43 | 0.71 |
Significative differences in dispersion have been detected between HI and R (p = 0.0053).
The following analyses allowed to detect species as characteristic of each condition. We used results from PRIMER to justify further their choice.
## cluster indicator_value probability
## bipalponephtys_neotena 1 0.6390 0.001
## macoma_calcarea 1 0.5583 0.004
## goniada_maculata 1 0.2917 0.028
## nematoda 2 0.6565 0.005
## echinarachnius_parma 2 0.6524 0.001
## crenella_decussata 2 0.5836 0.001
## ecrobia_truncata 2 0.3333 0.005
## mesodesma_arctatum 2 0.2667 0.016
## solariella_sp 2 0.2000 0.039
##
## Sum of probabilities = 55.401
##
## Sum of Indicator Values = 13.26
##
## Sum of Significant Indicator Values = 4.18
##
## Number of Significant Indicators = 9
##
## Significant Indicator Distribution
##
## 1 2
## 3 6
| average | sd | ratio | ava | avb | cumsum | |
|---|---|---|---|---|---|---|
| nematoda | 0.0743 | 0.0621 | 1.2 | 0.879 | 2.08 | 0.0852 |
| echinarachnius_parma | 0.0618 | 0.0632 | 0.977 | 0.361 | 1.59 | 0.156 |
| bipalponephtys_neotena | 0.0556 | 0.0485 | 1.15 | 1.78 | 0.193 | 0.22 |
| eudorellopsis_integra | 0.0358 | 0.0494 | 0.724 | 1.08 | 0.119 | 0.261 |
| crenella_decussata | 0.0346 | 0.0352 | 0.983 | 0.0289 | 1.03 | 0.3 |
| mesodesma_arctatum | 0.0338 | 0.064 | 0.528 | 0 | 0.751 | 0.339 |
| macoma_calcarea | 0.0316 | 0.0323 | 0.977 | 1.03 | 0.0462 | 0.375 |
| harpacticoida | 0.0283 | 0.0325 | 0.872 | 0.801 | 0.258 | 0.408 |
| phoxocephalus_holbolli | 0.0282 | 0.0329 | 0.857 | 0.411 | 0.658 | 0.44 |
| amphipoda | 0.0234 | 0.0274 | 0.852 | 0.483 | 0.346 | 0.467 |
| pholoe_sp | 0.0215 | 0.0249 | 0.866 | 0.413 | 0.434 | 0.492 |
| ameritella_agilis | 0.018 | 0.0252 | 0.715 | 0.132 | 0.451 | 0.512 |
| ecrobia_truncata | 0.0168 | 0.0321 | 0.525 | 0 | 0.575 | 0.532 |
| ennucula_tenuis | 0.0164 | 0.022 | 0.742 | 0.401 | 0.212 | 0.55 |
| ischyrocerus_anguipes | 0.0155 | 0.0327 | 0.475 | 0.406 | 0.13 | 0.568 |
| akanthophoreus_gracilis | 0.0154 | 0.0263 | 0.584 | 0.389 | 0.193 | 0.586 |
| hiatella_arctica | 0.015 | 0.0309 | 0.483 | 0.0747 | 0.368 | 0.603 |
| ostracoda | 0.013 | 0.0195 | 0.664 | 0.207 | 0.258 | 0.618 |
| cistenides_granulata | 0.0129 | 0.0202 | 0.641 | 0.233 | 0.212 | 0.633 |
| axinopsida_orbiculata | 0.0117 | 0.025 | 0.47 | 0.361 | 0 | 0.646 |
| thracia_septentrionalis | 0.0117 | 0.0185 | 0.633 | 0.183 | 0.266 | 0.66 |
| mysella_planulata | 0.0117 | 0.0263 | 0.445 | 0 | 0.333 | 0.673 |
| sabellidae_spp | 0.0112 | 0.0323 | 0.348 | 0.361 | 0.0462 | 0.686 |
| leucon_leucon_nasicoides | 0.0107 | 0.0237 | 0.454 | 0.294 | 0 | 0.698 |
| nephtyidae_spp | 0.0107 | 0.0176 | 0.608 | 0.274 | 0.0462 | 0.71 |
| orchomenella_minuta | 0.0102 | 0.019 | 0.539 | 0.0578 | 0.212 | 0.722 |
| nephtys_caeca | 0.00966 | 0.0194 | 0.497 | 0.116 | 0.119 | 0.733 |
| goniada_maculata | 0.00965 | 0.0165 | 0.585 | 0.286 | 0 | 0.744 |
| mytilus_sp | 0.0092 | 0.0268 | 0.342 | 0.287 | 0 | 0.755 |
| solariella_sp | 0.0089 | 0.0205 | 0.435 | 0 | 0.29 | 0.765 |
| pontoporeia_femorata | 0.00864 | 0.0245 | 0.352 | 0.225 | 0 | 0.775 |
| parvicardium_pinnulatum | 0.00863 | 0.0164 | 0.527 | 0.0578 | 0.227 | 0.785 |
| caprella_septentrionalis | 0.00844 | 0.029 | 0.291 | 0.34 | 0 | 0.795 |
| protomedeia_fasciata | 0.00786 | 0.0165 | 0.478 | 0.241 | 0 | 0.804 |
| lamprops_fuscatus | 0.00694 | 0.0128 | 0.543 | 0.207 | 0.0462 | 0.811 |
| pholoe_longa | 0.00605 | 0.0189 | 0.319 | 0.11 | 0.0732 | 0.818 |
| protomedeia_grandimana | 0.00592 | 0.0173 | 0.342 | 0.213 | 0 | 0.825 |
| astarte_sp | 0.00588 | 0.013 | 0.454 | 0.0747 | 0.119 | 0.832 |
| chone_sp | 0.00567 | 0.0282 | 0.201 | 0.107 | 0 | 0.838 |
| monoculopsis_longicornis | 0.00559 | 0.0144 | 0.388 | 0.125 | 0.0924 | 0.845 |
| ophelia_limacina | 0.00551 | 0.0112 | 0.494 | 0.0289 | 0.166 | 0.851 |
| glycera_sp | 0.00542 | 0.0171 | 0.317 | 0.116 | 0 | 0.857 |
| chaetodermatida | 0.00541 | 0.0117 | 0.462 | 0.144 | 0.0462 | 0.864 |
| nephtys_incisa | 0.00535 | 0.0126 | 0.423 | 0.104 | 0.0462 | 0.87 |
| maldanidae_spp | 0.00472 | 0.0156 | 0.303 | 0.113 | 0.0462 | 0.875 |
| aceroides_aceroides_latipes | 0.00452 | 0.0111 | 0.406 | 0.144 | 0 | 0.88 |
| quasimelita_formosa | 0.00447 | 0.0122 | 0.366 | 0.125 | 0 | 0.885 |
| sipuncula | 0.00428 | 0.0122 | 0.35 | 0 | 0.0924 | 0.89 |
| euchone_sp | 0.00426 | 0.0207 | 0.206 | 0.146 | 0 | 0.895 |
| anthozoa | 0.00409 | 0.0111 | 0.369 | 0 | 0.119 | 0.9 |
4. Univariate regressions
We used linear models for the all regressions on diversity indices. Outliers and correlated variables were removed from these analyses.
4.1. Simple regressions
These analyses have been do to explore the relationships between variables. As it is a huge number of results to interpret, only multiple regressions will be included in the article (see below).
Design 1
| om | gravel | sand | clay | |
|---|---|---|---|---|
| S_500 | -0.02767 | 0.01197 | -0.001571 | -0.02831 |
| N_500 | -0.0185 | -0.02098 | -0.02294 | 0.002618 |
| H_500 | -0.009374 | -0.0139 | 0.02245 | 0.02041 |
| J_500 | 0.02241 | -0.02941 | 0.005738 | 0.02843 |
| om | gravel | sand | clay | |
|---|---|---|---|---|
| S_500 | 0.8117 | 0.241 | 0.3378 | 0.8497 |
| N_500 | 0.5501 | 0.5997 | 0.6458 | 0.3034 |
| H_500 | 0.417 | 0.4757 | 0.1882 | 0.1973 |
| J_500 | 0.1883 | 0.9884 | 0.2806 | 0.1639 |
Design 2
Quitting from lines 291-310 (C1_analyses_17B.Rmd) Error in pandoc.table.return(…) : Wrong number of parameters (7 instead of 6) passed: justify De plus : Warning messages: 1: attribute variables are assumed to be spatially constant throughout all geometries 2: attribute variables are assumed to be spatially constant throughout all geometries 3: attribute variables are assumed to be spatially constant throughout all geometries 4: attribute variables are assumed to be spatially constant throughout all geometries 5: attribute variables are assumed to be spatially constant throughout all geometries 6: attribute variables are assumed to be spatially constant throughout all geometries 7: attribute variables are assumed to be spatially constant throughout all geometries 8: attribute variables are assumed to be spatially constant throughout all geometries Quitting from lines 291-310 (C1_analyses_17B.Rmd) Error in pandoc.table.return(…) : Wrong number of parameters (7 instead of 6) passed: justify
Furthermore, depth has been shown important for several parameters in the ANCOVAs, so here are the corresponding scatterplots.
4.2. Multiple regressions
This section presents analyses done (i) to determine which model (Design 1, Design 2) decribes the best the parameters and (ii) which variables are the most important to explain the parameters.
4.2.1. Best model selection
This step was not used here as both models were needed.
4.2.2. Significative variables selection
We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the tables below:
- for the model of Design 1
| Variable (or combination) | S_500 | N_500 | H_500 | J_500 |
|---|---|---|---|---|
| om | + | |||
| gravel | + | |||
| sand/silt | + | |||
| clay | + | |||
| Adjusted \(R^{2}\) | 0 | 0 | 0 | 0.06 |
- for the model of Design 2
| Variable (or combination) | S_500 | N_500 | H_500 | J_500 |
|---|---|---|---|---|
| arsenic | - | - | - | |
| cadmium | + | |||
| chromium/iron/manganese | - | - | - | |
| mercury | - | - | ||
| lead/copper/zinc | + | + | + | |
| Adjusted \(R^{2}\) | 0.24 | 0.26 | 0.24 | 0.09 |
Details of the regressions, with diagnostics and cross-validation, are summarized below.
Design 1
Species richness
## FULL MODEL
## Adjusted R2 is: -0.06
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 16.46 | 10.1 | 1.63 | 0.1132 | |
| om | -1.441 | 3.678 | -0.3918 | 0.6979 | |
| gravel | 3.448 | 11.32 | 0.3046 | 0.7627 | |
| sand | -6.499 | 9.858 | -0.6593 | 0.5146 | |
| clay | -7.884 | 17.56 | -0.4489 | 0.6566 |
| om | gravel | sand | clay | |
|---|---|---|---|---|
| VIF | 2.3 | 1.55 | 2.93 | 1.76 |
## REDUCED MODEL
## Adjusted R2 is: 0
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 10.81 | 0.8365 | 12.92 | 7.05e-15 | * * * |
Quitting from lines 363-367 (C1_analyses_17B.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites
## RMSE for the full model: 54.74662
## RMSE for the reduced model: 4.980853
Total abundance
## FULL MODEL
## Adjusted R2 is: -0.06
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 210.2 | 164.1 | 1.281 | 0.2098 | |
| om | -48.57 | 59.75 | -0.8128 | 0.4225 | |
| gravel | -52.99 | 183.9 | -0.2882 | 0.7751 | |
| sand | -117.8 | 160.2 | -0.7354 | 0.4676 | |
| clay | -323.7 | 285.3 | -1.135 | 0.2652 |
| om | gravel | sand | clay | |
|---|---|---|---|---|
| VIF | 2.3 | 1.55 | 2.93 | 1.76 |
## REDUCED MODEL
## Adjusted R2 is: 0
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 81.67 | 13.57 | 6.018 | 7.313e-07 | * * * |
Quitting from lines 372-376 (C1_analyses_17B.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : Warning messages: 1: In if (is.na(data_CV)) { : la condition a une longueur > 1 et seul le premier élément est utilisé 2: In CVlm(data = data_CV, form.lm = full_model, m = 5, main = “Full model”, :
As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate
3: In CVlm(data = data_CV, form.lm = reduced_model, m = 5, main = “Reduced model”, :
As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate
## RMSE for the full model: 1431.399
## RMSE for the reduced model: 86.93245
Shannon index
## FULL MODEL
## Adjusted R2 is: -0.03
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 0.8243 | 1.189 | 0.6932 | 0.4934 | |
| om | 0.357 | 0.433 | 0.8246 | 0.4159 | |
| gravel | 1.322 | 1.333 | 0.9921 | 0.3288 | |
| sand | 0.6177 | 1.161 | 0.5323 | 0.5983 | |
| clay | 2.295 | 2.067 | 1.11 | 0.2756 |
| om | gravel | sand | clay | |
|---|---|---|---|---|
| VIF | 2.3 | 1.55 | 2.93 | 1.76 |
## REDUCED MODEL
## Adjusted R2 is: 0
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 1.627 | 0.1001 | 16.25 | 6.987e-18 | * * * |
Quitting from lines 381-385 (C1_analyses_17B.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : Warning messages: 1: In if (is.na(data_CV)) { : la condition a une longueur > 1 et seul le premier élément est utilisé 2: In CVlm(data = data_CV, form.lm = full_model, m = 5, main = “Full model”, :
As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate
3: In CVlm(data = data_CV, form.lm = reduced_model, m = 5, main = “Reduced model”, :
As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate
## RMSE for the full model: 17.33173
## RMSE for the reduced model: 0.6033001
Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: 0.06
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 0.04865 | 0.3676 | 0.1323 | 0.8956 | |
| om | 0.2648 | 0.1339 | 1.978 | 0.05686 | |
| gravel | 0.583 | 0.4119 | 1.415 | 0.1669 | |
| sand | 0.5808 | 0.3588 | 1.619 | 0.1156 | |
| clay | 1.303 | 0.6391 | 2.038 | 0.05012 |
| om | gravel | sand | clay | |
|---|---|---|---|---|
| VIF | 2.3 | 1.55 | 2.93 | 1.76 |
## REDUCED MODEL
## Adjusted R2 is: 0.06
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 0.04865 | 0.3676 | 0.1323 | 0.8956 | |
| om | 0.2648 | 0.1339 | 1.978 | 0.05686 | |
| gravel | 0.583 | 0.4119 | 1.415 | 0.1669 | |
| sand | 0.5808 | 0.3588 | 1.619 | 0.1156 | |
| clay | 1.303 | 0.6391 | 2.038 | 0.05012 |
| om | gravel | sand | clay | |
|---|---|---|---|---|
| VIF | 2.3 | 1.55 | 2.93 | 1.76 |
## RMSE for the full model: 5.628968
## RMSE for the reduced model: 5.628968
Design 2
Species richness
## FULL MODEL
## Adjusted R2 is: 0.21
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 23.8 | 5.102 | 4.666 | 0.0002584 | * * * |
| arsenic | -1.844 | 1.359 | -1.356 | 0.1938 | |
| cadmium | -24.45 | 42.5 | -0.5752 | 0.5731 | |
| chromium | -0.1864 | 0.171 | -1.09 | 0.2917 | |
| mercury | -249 | 178.1 | -1.398 | 0.1812 | |
| lead | 2.31 | 1.714 | 1.347 | 0.1966 |
| arsenic | cadmium | chromium | mercury | lead | |
|---|---|---|---|---|---|
| VIF | 2.11 | 1.6 | 3.14 | 1.22 | 3.69 |
## REDUCED MODEL
## Adjusted R2 is: 0.24
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 21.94 | 3.86 | 5.683 | 2.694e-05 | * * * |
| arsenic | -1.859 | 1.332 | -1.396 | 0.1807 | |
| chromium | -0.2166 | 0.1595 | -1.358 | 0.1923 | |
| mercury | -228.2 | 170.9 | -1.335 | 0.1994 | |
| lead | 2.211 | 1.672 | 1.323 | 0.2035 |
| arsenic | chromium | mercury | lead | |
|---|---|---|---|---|
| VIF | 2.11 | 2.99 | 1.2 | 3.67 |
## RMSE for the full model: 6.476836
## RMSE for the reduced model: 5.727678
Total abundance
## FULL MODEL
## Adjusted R2 is: 0.18
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 166.3 | 86.35 | 1.926 | 0.07209 | |
| arsenic | -18.89 | 23 | -0.8212 | 0.4236 | |
| cadmium | 934.9 | 719.4 | 1.3 | 0.2121 | |
| chromium | -0.3244 | 2.894 | -0.1121 | 0.9121 | |
| mercury | -4735 | 3014 | -1.571 | 0.1358 | |
| lead | -4.739 | 29.02 | -0.1633 | 0.8723 |
| arsenic | cadmium | chromium | mercury | lead | |
|---|---|---|---|---|---|
| VIF | 2.11 | 1.6 | 3.14 | 1.22 | 3.69 |
## REDUCED MODEL
## Adjusted R2 is: 0.26
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 173.6 | 76.52 | 2.268 | 0.03584 | * |
| arsenic | -24.78 | 14.68 | -1.688 | 0.1086 | |
| cadmium | 795.1 | 568.3 | 1.399 | 0.1788 | |
| mercury | -5175 | 2583 | -2.004 | 0.06036 |
| arsenic | cadmium | mercury | |
|---|---|---|---|
| VIF | 1.42 | 1.33 | 1.11 |
## RMSE for the full model: 100.7356
## RMSE for the reduced model: 90.95802
Shannon index
## FULL MODEL
## Adjusted R2 is: 0.24
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 3.153 | 0.598 | 5.273 | 7.572e-05 | * * * |
| arsenic | -0.2436 | 0.1593 | -1.529 | 0.1458 | |
| cadmium | -4.38 | 4.982 | -0.8792 | 0.3923 | |
| chromium | -0.03856 | 0.02004 | -1.924 | 0.07233 | |
| mercury | -26.63 | 20.87 | -1.276 | 0.2203 | |
| lead | 0.4741 | 0.2009 | 2.359 | 0.03135 | * |
| arsenic | cadmium | chromium | mercury | lead | |
|---|---|---|---|---|---|
| VIF | 2.11 | 1.6 | 3.14 | 1.22 | 3.69 |
## REDUCED MODEL
## Adjusted R2 is: 0.24
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 2.53 | 0.3833 | 6.602 | 3.363e-06 | * * * |
| arsenic | -0.2266 | 0.1584 | -1.43 | 0.1698 | |
| chromium | -0.04207 | 0.01902 | -2.212 | 0.04012 | * |
| lead | 0.3896 | 0.191 | 2.04 | 0.05632 |
| arsenic | chromium | lead | |
|---|---|---|---|
| VIF | 2.09 | 2.98 | 3.51 |
## RMSE for the full model: 0.8342848
## RMSE for the reduced model: 0.6644866
Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: -0.07
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 0.8522 | 0.2505 | 3.403 | 0.003639 | * * |
| arsenic | -0.018 | 0.06672 | -0.2697 | 0.7908 | |
| cadmium | -0.2464 | 2.086 | -0.1181 | 0.9075 | |
| chromium | -0.01462 | 0.008394 | -1.741 | 0.1008 | |
| mercury | 0.6441 | 8.743 | 0.07367 | 0.9422 | |
| lead | 0.1317 | 0.08416 | 1.565 | 0.1371 |
| arsenic | cadmium | chromium | mercury | lead | |
|---|---|---|---|---|---|
| VIF | 2.11 | 1.6 | 3.14 | 1.22 | 3.69 |
## REDUCED MODEL
## Adjusted R2 is: 0.09
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 0.8433 | 0.1476 | 5.712 | 1.663e-05 | * * * |
| chromium | -0.01498 | 0.007328 | -2.045 | 0.055 | |
| lead | 0.1212 | 0.06253 | 1.938 | 0.06757 |
| chromium | lead | |
|---|---|---|
| VIF | 2.98 | 2.98 |
## RMSE for the full model: 0.3580101
## RMSE for the reduced model: 0.3273077
5. Multivariate regressions
Independant variables are habitat parameters or heavy metal concentrations, dependant variables are species abundances. Outliers and correlated variables have been excluded from the analysis.
This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.
Design 1
Variables selected by the DistLM procedure have a \(R^{2}\) of 0.23.
Design 2
Variables selected by the DistLM procedure have a \(R^{2}\) of 0.13.